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Image Generation

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하위 태스크: 23
평가표: 93
보존된 평가 기록: 870

Image Generation (synthesis) is the task of generating new images from an existing dataset.

Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/rocketappslab/bdmm

공식 코드

Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer

https://github.com/rockeyben/dccf

공식 코드

DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization

https://github.com/rockeycoss/spo

공식 코드

Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

https://github.com/rockwenjj/syreanet

공식 코드

SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

https://github.com/roeiherz/CanonicalSg2Im

공식 코드

Learning Canonical Representations for Scene Graph to Image Generation

https://github.com/rohitgandikota/erasing

공식 코드

Erasing Concepts from Diffusion Models

https://github.com/rohitgandikota/sliders

공식 코드

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models

https://github.com/romanjacome99/ld_gan

공식 코드

LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization

https://github.com/rongyaofang/got

공식 코드

GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing

https://github.com/rongyaofang/puma

공식 코드

PUMA: Empowering Unified MLLM with Multi-granular Visual Generation

https://github.com/rootsue/causal-rsgan

공식 코드

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

https://github.com/rothk/Stabilizing_GANs

공식 코드

Stabilizing Training of Generative Adversarial Networks through Regularization

https://github.com/royee182/dpl

공식 코드

Dual Path Learning for Domain Adaptation of Semantic Segmentation

https://github.com/royg27/BIGRoC

공식 코드

BIGRoC: Boosting Image Generation via a Robust Classifier

https://github.com/royira/syntax-guided-generation

공식 코드

Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment

https://github.com/roymiles/vkd

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$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections

https://github.com/royorel/FFHQ-Aging-Dataset

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Lifespan Age Transformation Synthesis

https://github.com/royorel/Lifespan_Age_Transformation_Synthesis

공식 코드

Lifespan Age Transformation Synthesis

https://github.com/royorel/StyleSDF

공식 코드

StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation

https://github.com/rskarkare/Robust-Hadamard-Autoencoders

공식 코드

Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders

https://github.com/rsummers11/CADLab

공식 코드

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

https://github.com/rt219/latentguard

공식 코드

Latent Guard: a Safety Framework for Text-to-image Generation

https://github.com/rtqichen/residual-flows

공식 코드

Residual Flows for Invertible Generative Modeling

https://github.com/rtqichen/style-swap

공식 코드

Fast Patch-based Style Transfer of Arbitrary Style

https://github.com/rubencart/uscoco

공식 코드

Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected Situations

https://github.com/ruchikachavhan/concept-prune

공식 코드

ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning

https://github.com/ruihangao/tactiledreamfusion

공식 코드

Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation

https://github.com/ruiliu-ai/DivCo

공식 코드

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

https://github.com/ruiqigao/recovery_likelihood

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Learning Energy-Based Models by Diffusion Recovery Likelihood

https://github.com/ruiyi-w/learning-hazing-to-dehazing

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Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing

https://github.com/ruizhe-l/tgb-gan

공식 코드

Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI

https://github.com/ruizvitor/ANDA

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ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection

https://github.com/runwayml/guided-inpainting

공식 코드

Towards Unified Keyframe Propagation Models

https://github.com/ruveydayilmaz0/cascaded_diffusion

공식 코드

Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation

https://github.com/rvignav/diffusion-tts

공식 코드

Test-Time Scaling of Diffusion Models via Noise Trajectory Search

https://github.com/ry85/Stochastic-Talking-Face-Generation-Using-Latent-Distribution-Matching

공식 코드

Stochastic Talking Face Generation Using Latent Distribution Matching

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